Psychological primitives can make sense of biopsychosocial factor complexity in psychopathology

Abstract

Background Many agree that the biopsychosocial contributions to psychopathology are complex, yet it is unclear how we can make sense of this complexity. One approach is to reduce this complexity to a few necessary and sufficient biopsychosocial factors; although this approach is easy to understand, it has little explanatory power. Another approach is to fully embrace complexity, proposing that each instance of psychopathology is caused by a partially unique set of biopsychosocial factors; this approach has high explanatory power, but is impossible to comprehend. Due to deficits in either explanatory power or comprehensibility, both approaches limit our ability to make substantial advances in understanding, predicting, and preventing psychopathology. Thus, how can we make sense of biopsychosocial factor complexity? Main text There is a third possible approach that can resolve this dilemma, with high explanatory power and high comprehensibility. This approach involves understanding, predicting, and preventing psychopathology in terms of a small set of psychological primitives rather than biopsychosocial factors. Psychological primitives are the fundamental and irreducible elements of the mind, mediating all biopsychosocial factor influences on psychopathology. All psychological phenomena emerge from these primitives. Over the past decade, this approach has been successfully applied within basic psychological science, most notably affective science. It explains the sum of the evidence in affective science and has generated several novel research directions. This approach is equally applicable to psychopathology. The primitive-based approach does not eliminate the role of biopsychosocial factors, but rather recasts them as indeterminate causal influences on psychological primitives. In doing so, it reframes research away from factor-based questions (e.g., which situations cause suicide?) and toward primitive-based questions (e.g., how are suicidality concepts formed, altered, activated, and implemented?). This is a valuable shift because factor-based questions have indeterminate answers (e.g., infinite situations could cause suicide) whereas primitive-based questions have determinate answers (e.g., there are specific processes that undergird all concepts). Conclusion The primitive-based approach accounts for biopsychosocial complexity, ties clinical science more directly to basic psychological science, and could facilitate progress in understanding, predicting, and preventing psychopathology.

Systems all the way down: embracing complexity in mental health research

“…The contributions from McLaughlin et al [ 8 ], Ioannidis et al [ 9 ], and Fritz et al [ 10 ] provide an extensive overview of how mental disorders can emerge from the dynamic interactions among components across levels of analysis. Franklin [ 11 ] further explores the concept of emergence by shifting the focus to even more elementary components that give rise to psychological phenomena. These components, known as psychological primitives, represent the fundamental and irreducible building blocks of psychological experiences, such as core affect, exteroception, and conceptual knowledge.…”

Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research

“…The idea that psychiatric phenomena are highly complex and therefore cannot be explained by a small number of variables with linear relationships has seen increased support in recent years (Franklin, 2019;Kendler, 2005Kendler, , 2019Mitchell, 2015). Perhaps one of the most complex phenomena is suicide, an outcome that is difficult to predict and appears to have many causes.…”

Cautions, concerns, and future directions for using machine learning in relation to mental health problems and clinical and forensic risks: A brief comment on “Model complexity improves the prediction of nonsuicidal self-injury” (Fox et al., 2019).

“…It is noteworthy that few to no published studies have tested the replicability of ML algorithms on independent datasets. Indeed, in a recent paper outlining future directions for the suicide attempt literature and for research on psychological problems in general, it has previously been argued that ML models struggle to replicate in new samples (Franklin, 2019). Replication is always important (Siddaway et al, 2019b); however, given the concerns outlined in this Brief Comment, ML is an area where replication seems fundamental.…”